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Journal articles on the topic 'DNA microarrays gene expression'

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Published: 4 June 2021

Last updated: 11 February 2022

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1

Russell, S. "DNA Microarrays: Gene Expression Applications." Heredity 89, no. 5 (October 28, 2002): 402. http://dx.doi.org/10.1038/sj.hdy.6800150.

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Harrington, Christina A., Carsten Rosenow, and Jacques Retief. "Monitoring gene expression using DNA microarrays." Current Opinion in Microbiology 3, no. 3 (June 2000): 285–91. http://dx.doi.org/10.1016/s1369-5274(00)00091-6.

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3

Wu, Shu-Hsing, Katrina Ramonell, Jeremy Gollub, and Shauna Somerville. "Plant gene expression profiling with DNA microarrays." Plant Physiology and Biochemistry 39, no. 11 (November 2001): 917–26. http://dx.doi.org/10.1016/s0981-9428(01)01322-5.

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King, Hadley C., and Animesh A. Sinha. "Gene Expression Profile Analysis by DNA Microarrays." JAMA 286, no. 18 (November 14, 2001): 2280. http://dx.doi.org/10.1001/jama.286.18.2280.

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Ramaswamy, Sridhar, and Todd R. Golub. "DNA Microarrays in Clinical Oncology." Journal of Clinical Oncology 20, no. 7 (April 1, 2002): 1932–41. http://dx.doi.org/10.1200/jco.2002.20.7.1932.

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ABSTRACT: Aberrant gene expression is critical for tumor initiation and progression. However, we lack a comprehensive understanding of all genes that are aberrantly expressed in human cancer. Recently, DNA microarrays have been used to obtain global views of human cancer gene expression and to identify genetic markers that might be important for diagnosis and therapy. We review clinical applications of these novel tools, discuss some important recent studies, identify promising avenues of research in this emerging field of study, and discuss the likely impact that expression profiling will hav

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Chinnaivan, Kavitha M., Arun Sreekumar, Walter M. Whitehouse, Naresh T. Gunaratnam, Stuart A. Winston, and Arul M. Chinnaiyan. "Profiling gene expression in atherosclerosis using DNA microarrays." Journal of the American College of Cardiology 39 (March 2002): 233. http://dx.doi.org/10.1016/s0735-1097(02)81038-5.

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Weindruch, Richard, Tsuyoshi Kayo, Cheol-Koo Lee, and Tomas A. Prolla. "Gene expression profiling of aging using DNA microarrays." Mechanisms of Ageing and Development 123, no. 2-3 (January 2002): 177–93. http://dx.doi.org/10.1016/s0047-6374(01)00344-x.

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Haviv, Izhak, and Ian G. Campbell. "DNA microarrays for assessing ovarian cancer gene expression." Molecular and Cellular Endocrinology 191, no. 1 (May 2002): 121–26. http://dx.doi.org/10.1016/s0303-7207(02)00063-1.

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Landgraf, Jeff, Ellen Wisman, Robert Schaffer, Monica Accerbi, Vernadette Simon, Matt Larson, and Pam Green. "Gene expression profiling in Arabidopsis using DNA microarrays." Nature Genetics 23, S3 (November 1999): 56. http://dx.doi.org/10.1038/14344.

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Arcellana-Panlilio, Mayi, and Stephen M. Robbins. "I. Global gene expression profiling using DNA microarrays." American Journal of Physiology-Gastrointestinal and Liver Physiology 282, no. 3 (March 1, 2002): G397—G402. http://dx.doi.org/10.1152/ajpgi.00519.2001.

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Having the complete human genomic sequence poses a new challenge: to use genomic structural information to display and analyze biological processes on a genome-wide scale to assign gene function. DNA microarrays are a miniaturized, ordered arrangement of nucleic acid fragments from individual genes located at defined positions on a solid support, enabling the analysis of thousands of genes in parallel by specific hybridization. This review describes technical aspects, discusses relevant applications, and suggests factors affecting the use of this technology and how it fits in the grand scheme

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Travensolo, Regiane F., Lucia M. Carareto-Alves, Maria V. C. G. Costa, Tiago J. S. Lopes, Emanuel Carrilho, and Eliana G. M. Lemos. "Xylella fastidiosa gene expression analysis by DNA microarrays." Genetics and Molecular Biology 32, no. 2 (May 1, 2009): 340–53. http://dx.doi.org/10.1590/s1415-47572009005000038.

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Whipple, Mark Eliot, and Winston Patrick Kuo. "DNA Microarrays in Otolaryngology-Head and Neck Surgery." Otolaryngology–Head and Neck Surgery 127, no. 3 (September 2002): 196–204. http://dx.doi.org/10.1067/mhn.2002.127383.

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OBJECTIVES: Our goal was to review the technologies underlying DNA microarrays and to explore their use in otolaryngology-head and neck surgery. STUDY DESIGN: The current literature relating to microarray technology and methodology is reviewed, specifically the use of DNA microarrays to characterize gene expression. Bioinformatics involves computational and statistical methods to extract, organize, and analyze the huge amounts of data produced by microarray experiments. The means by which these techniques are being applied to otolaryngology-head and neck surgery are outlined. RESULTS: Microarr

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Widłak, Piotr. "DNA microarrays, a novel approach in studies of chromatin structure." Acta Biochimica Polonica 51, no. 1 (March 31, 2004): 1–8. http://dx.doi.org/10.18388/abp.2004_3592.

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The DNA microarray technology delivers an experimental tool that allows surveying expression of genetic information on a genome-wide scale at the level of single genes--for the new field termed functional genomics. Gene expression profiling--the primary application of DNA microarrays technology--generates monumental amounts of information concerning the functioning of genes, cells and organisms. However, the expression of genetic information is regulated by a number of factors that cannot be directly targeted by standard gene expression profiling. The genetic material of eukaryotic cells is pa

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Chavan, Preeti, Kalpana Joshi, and Bhushan Patwardhan. "DNA Microarrays in Herbal Drug Research." Evidence-Based Complementary and Alternative Medicine 3, no. 4 (2006): 447–57. http://dx.doi.org/10.1093/ecam/nel075.

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Natural products are gaining increased applications in drug discovery and development. Being chemically diverse they are able to modulate several targets simultaneously in a complex system. Analysis of gene expression becomes necessary for better understanding of molecular mechanisms. Conventional strategies for expression profiling are optimized for single gene analysis. DNA microarrays serve as suitable high throughput tool for simultaneous analysis of multiple genes. Major practical applicability of DNA microarrays remains in DNA mutation and polymorphism analysis. This review highlights ap

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KURELLA, MANJULA, LI-LI HSIAO, TAKUMI YOSHIDA, JEFFREY D. RANDALL, GARY CHOW, SATINDER S. SARANG, RODERICK V. JENSEN, and STEVEN R. GULLANS. "DNA Microarray Analysis of Complex Biologic Processes." Journal of the American Society of Nephrology 12, no. 5 (May 2001): 1072–78. http://dx.doi.org/10.1681/asn.v1251072.

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Abstract. DNA microarrays, or gene chips, allow surveys of gene expression, (i.e., mRNA expression) in a highly parallel and comprehensive manner. The pattern of gene expression produced, known as the expression profile, depicts the subset of gene transcripts expressed in a cell or tissue. At its most fundamental level, the expression profile can address qualitatively which genes are expressed in disease states. However, with the aid of bioinformatics tools such as cluster analysis, self-organizing maps, and principle component analysis, more sophisticated questions can be answered. Microarray

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Rosenfeld, Simon. "Do DNA Microarrays Tell the Story of Gene Expression?" Gene Regulation and Systems Biology 4 (January 2010): GRSB.S4657. http://dx.doi.org/10.4137/grsb.s4657.

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Ockers, Sandra, Douglas K. Price, and William D. Figg. "DNA microarrays: Tissue removal and processing affects gene expression." Cancer Biology & Therapy 5, no. 12 (December 31, 2006): 1608–9. http://dx.doi.org/10.4161/cbt.5.12.3547.

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van Hal, Nicole L. W., Oscar Vorst, Adèle M. M. L. van Houwelingen, Esther J. Kok, Ad Peijnenburg, Asaph Aharoni, Arjen J. van Tunen, and Jaap Keijer. "The application of DNA microarrays in gene expression analysis." Journal of Biotechnology 78, no. 3 (March 2000): 271–80. http://dx.doi.org/10.1016/s0168-1656(00)00204-2.

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Benson, M., P. A. Svensson, B. Carlsson, M. Jernås, J. Reinholdt, L. O. Cardell, and L. Carlsson. "DNA microarrays to study gene expression in allergic airways." Clinical & Experimental Allergy 32, no. 2 (February 2002): 301–8. http://dx.doi.org/10.1046/j.1365-2222.2002.01300.x.

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Karakach, Tobias K., Robert M. Flight, Susan E. Douglas, and Peter D. Wentzell. "An introduction to DNA microarrays for gene expression analysis." Chemometrics and Intelligent Laboratory Systems 104, no. 1 (November 2010): 28–52. http://dx.doi.org/10.1016/j.chemolab.2010.04.003.

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21

Ju, Xin-Sheng, and Martin Zenke. "Gene expression profiling of dendritic cells by DNA microarrays." Immunobiology 209, no. 1-2 (August 2004): 155–61. http://dx.doi.org/10.1016/j.imbio.2004.02.005.

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22

Valente, Eduardo, and Miguel Rocha. "Integrating data from heterogeneous DNA microarray platforms." Journal of Integrative Bioinformatics 12, no. 4 (December 1, 2015): 39–55. http://dx.doi.org/10.1515/jib-2015-281.

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Summary DNA microarrays are one of the most used technologies for gene expression measurement. However, there are several distinct microarray platforms, from different manufacturers, each with its own measurement protocol, resulting in data that can hardly be compared or directly integrated. Data integration from multiple sources aims to improve the assertiveness of statistical tests, reducing the data dimensionality problem. The integration of heterogeneous DNA microarray platforms comprehends a set of tasks that range from the re-annotation of the features used on gene expression, to data no

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Wang, Wei, Larry Reitzer, David A. Rasko, Melanie M. Pearson, Robert J. Blick, Cassie Laurence, and Eric J. Hansen. "Metabolic Analysis of Moraxella catarrhalis and the Effect of Selected In Vitro Growth Conditions on Global Gene Expression." Infection and Immunity 75, no. 10 (July 9, 2007): 4959–71. http://dx.doi.org/10.1128/iai.00073-07.

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ABSTRACT The nucleotide sequence from the genome of Moraxella catarrhalis ATCC 43617 was annotated and used both to assess the metabolic capabilities and limitations of this bacterium and to design probes for a DNA microarray. An absence of gene products for utilization of exogenous carbohydrates was noteworthy and could be correlated with published phenotypic data. Gene products necessary for aerobic energy generation were present, as were a few gene products generally ascribed to anaerobic systems. Enzymes for synthesis of all amino acids except proline and arginine were present. M. catarrha

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Yuvaraj, K., and D. Manjula. "A performance analysis of clustering based algorithms for the microarray gene expression data." International Journal of Engineering & Technology 7, no. 2.21 (April 20, 2018): 201. http://dx.doi.org/10.14419/ijet.v7i2.21.12172.

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Current advancements in microarray technology permit simultaneous observing of the expression levels of huge number of genes over various time points. Microarrays have obtained amazing implication in the field of bioinformatics. It includes an ordered set of huge different Deoxyribonucleic Acid (DNA) sequences that can be used to measure both DNA as well as Ribonucleic Acid (RNA) dissimilarities. The Gene Expression (GE) summary aids in understanding the basic cause of gene activities, the growth of genes, determining recent disorders like cancer and as well analysing their molecular pharmacol

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GERHOLD, DAVID, MEIQING LU, JIAN XU, CHRISTOPHER AUSTIN, C. THOMAS CASKEY, and THOMAS RUSHMORE. "Monitoring expression of genes involved in drug metabolism and toxicology using DNA microarrays." Physiological Genomics 5, no. 4 (April 27, 2001): 161–70. http://dx.doi.org/10.1152/physiolgenomics.2001.5.4.161.

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Oligonucleotide DNA microarrays were investigated for utility in measuring global expression profiles of drug metabolism genes. This study was performed to investigate the feasibility of using microarray technology to minimize the long, expensive process of testing drug candidates for safety in animals. In an evaluation of hybridization specificity, microarray technology from Affymetrix distinguished genes up to a threshold of ∼90% DNA identity. Oligonucleotides representing human cytochrome P-450 gene CYP3A5 showed heterologous hybridization to CYP3A4 and CYP3A7 RNAs. These genes could be cle

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Kang, Seung-Hoon, Jianqiang Huang, Han-Na Lee, Yoon-Ah Hur, Stanley N. Cohen, and Eung-Soo Kim. "Interspecies DNA Microarray Analysis Identifies WblA as a Pleiotropic Down-Regulator of Antibiotic Biosynthesis in Streptomyces." Journal of Bacteriology 189, no. 11 (April 6, 2007): 4315–19. http://dx.doi.org/10.1128/jb.01789-06.

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ABSTRACT Using Streptomyces coelicolor microarrays to discover regulators of gene expression in other Streptomyces species, we identified wblA, a whiB-like gene encoding a putative transcription factor, as a down-regulator of doxorubicin biosynthesis in Streptomyces peucetius. Further analysis revealed that wblA functions pleiotropically to control antibiotic production and morphological differentiation in streptomycetes. Our results reveal a novel biological role for wblA and show the utility of interspecies microarray analysis for the investigation of streptomycete gene expression.

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Dennis, Philip, Elizabeth A. Edwards, Steven N. Liss, and Roberta Fulthorpe. "Monitoring Gene Expression in Mixed Microbial Communities by Using DNA Microarrays." Applied and Environmental Microbiology 69, no. 2 (February 2003): 769–78. http://dx.doi.org/10.1128/aem.69.2.769-778.2003.

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ABSTRACT A DNA microarray to monitor the expression of bacterial metabolic genes within mixed microbial communities was designed and tested. Total RNA was extracted from pure and mixed cultures containing the 2,4-dichlorophenoxyacetic acid (2,4-D)-degrading bacterium Ralstonia eutropha JMP134, and the inducing agent 2,4-D. Induction of the 2,4-D catabolic genes present in this organism was readily detected 4, 7, and 24 h after the addition of 2,4-D. This strain was diluted into a constructed mixed microbial community derived from a laboratory scale sequencing batch reactor. Induction of two of

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Hayden, Patrick S., Ashraf El-Meanawy, Jeffrey R. Schelling, and John R. Sedor. "DNA expression analysis: serial analysis of gene expression, microarrays and kidney disease." Current Opinion in Nephrology and Hypertension 12, no. 4 (July 2003): 407–14. http://dx.doi.org/10.1097/00041552-200307000-00009.

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29

Liesegang, Thomas J. "Gene expression profile analysis by DNA microarrays. Promise and pitfalls." American Journal of Ophthalmology 133, no. 5 (May 2002): 739. http://dx.doi.org/10.1016/s0002-9394(02)01446-0.

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Luo, Jun, William B. Isaacs, Jeffrey M. Trent, and David J. Duggan. "Looking Beyond Morphology: Cancer Gene Expression Profiling Using DNA Microarrays." Cancer Investigation 21, no. 6 (January 2003): 937–49. http://dx.doi.org/10.1081/cnv-120025096.

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Hayes, D. Neil, Stefano Monti, Giovanni Parmigiani, C. Blake Gilks, Katsuhiko Naoki, Arindam Bhattacharjee, Mark A. Socinski, Charles Perou, and Matthew Meyerson. "Gene Expression Profiling Reveals Reproducible Human Lung Adenocarcinoma Subtypes in Multiple Independent Patient Cohorts." Journal of Clinical Oncology 24, no. 31 (November 1, 2006): 5079–90. http://dx.doi.org/10.1200/jco.2005.05.1748.

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Purpose Published reports suggest that DNA microarrays identify clinically meaningful subtypes of lung adenocarcinomas not recognizable by other routine tests. This report is an investigation of the reproducibility of the reported tumor subtypes. Methods Three independent cohorts of patients with lung cancer were evaluated using a variety of DNA microarray assays. Using the integrative correlations method, a subset of genes was selected, the reliability of which was acceptable across the different DNA microarray platforms. Tumor subtypes were selected using consensus clustering and genes disti

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Weidenhammer, Elaine M., Brenda F. Kahl, Ling Wang, Larry Wang, Melanie Duhon, Jo Ann Jackson, Matthew Slater, and Xiao Xu. "Multiplexed, Targeted Gene Expression Profiling and Genetic Analysis on Electronic Microarrays." Clinical Chemistry 48, no. 11 (November 1, 2002): 1873–82. http://dx.doi.org/10.1093/clinchem/48.11.1873.

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Abstract Background: Electronic microarrays comprise independent microelectrode test sites that can be electronically biased positive or negative, or left neutral, to move and concentrate charged molecules such as DNA and RNA to one or more test sites. We developed a protocol for multiplexed gene expression profiling of mRNA targets that uses electronic field-facilitated hybridization on electronic microarrays. Methods: A multiplexed, T7 RNA polymerase-mediated amplification method was used for expression profiling of target mRNAs from total cellular RNA; targets were detected by hybridization

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Calvo-Dmgz, D., J. F. Gálvez, D. Glez-Peña, S. Gómez-Meire, and F. Fdez-Riverola. "Using Variable Precision Rough Set for Selection and Classification of Biological Knowledge Integrated in DNA Gene Expression." Journal of Integrative Bioinformatics 9, no. 3 (December 1, 2012): 1–17. http://dx.doi.org/10.1515/jib-2012-199.

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Summary DNA microarrays have contributed to the exponential growth of genomic and experimental data in the last decade. This large amount of gene expression data has been used by researchers seeking diagnosis of diseases like cancer using machine learning methods. In turn, explicit biological knowledge about gene functions has also grown tremendously over the last decade. This work integrates explicit biological knowledge, provided as gene sets, into the classication process by means of Variable Precision Rough Set Theory (VPRS). The proposed model is able to highlight which part of the provid

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Virtanen, Carl, and Mark Takahashi. "Muscling in on microarrays." Applied Physiology, Nutrition, and Metabolism 33, no. 1 (February 2008): 124–29. http://dx.doi.org/10.1139/h07-150.

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Adaptations that are the result of exercise require a multitude of changes at the level of gene expression. The mechanisms involved in regulating these changes are many, and can occur at various points in the pathways that affect gene expression. The completion of the human genome sequence, along with the genomes of related species, has provided an enormous amount of information to help dissect and understand these pathways. High-throughput methods, such as DNA microarrays, were the first on the scene to take advantage of this wealth of information. A new generation of microarrays has now take

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Álvarez, Patricia, Pilar Sáenz, David Arteta, Antonio Martínez, Miguel Pocoví, Laureano Simón, and Pilar Giraldo. "Transcriptional Profiling of Hematologic Malignancies with a Low-Density DNA Microarray." Clinical Chemistry 53, no. 2 (February 1, 2007): 259–67. http://dx.doi.org/10.1373/clinchem.2006.075887.

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Abstract Background: High-density microarrays are powerful tools for expression analysis of thousands of genes simultaneously; however, experience with low-density microarrays in gene expression studies has been limited. Methods: We developed an optimized procedure for gene expression analysis based on a microarray containing 538 oligonucleotides and used this procedure to analyze neoplastic cell lines and whole-blood samples from healthy individuals and patients with different hematologic neoplasias. Hierarchical clustering and the Welch t-test with adjusted P values were used for data analys

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Koczor, Christopher A., Eva K. Lee, Rebecca A. Torres, Amy Boyd, J. David Vega, Karan Uppal, Fan Yuan, Earl J. Fields, Allen M. Samarel, and William Lewis. "Detection of differentially methylated gene promoters in failing and nonfailing human left ventricle myocardium using computation analysis." Physiological Genomics 45, no. 14 (July 15, 2013): 597–605. http://dx.doi.org/10.1152/physiolgenomics.00013.2013.

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Human dilated cardiomyopathy (DCM) is characterized by congestive heart failure and altered myocardial gene expression. Epigenetic changes, including DNA methylation, are implicated in the development of DCM but have not been studied extensively. Clinical human DCM and nonfailing control left ventricle samples were individually analyzed for DNA methylation and expressional changes. Expression microarrays were used to identify 393 overexpressed and 349 underexpressed genes in DCM (GEO accession number: GSE43435 ). Gene promoter microarrays were utilized for DNA methylation analysis, and the res

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Mao, Mao, Matt C. Biery, Sumire V. Kobayashi, Terry Ward, Greg Schimmack, Julja Burchard, Janell M. Schelter, Hongyue Dai, Yudong D. He, and Peter S. Linsley. "T lymphocyte activation gene identification by coregulated expression on DNA microarrays." Genomics 83, no. 6 (June 2004): 989–99. http://dx.doi.org/10.1016/j.ygeno.2003.12.019.

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Johnson, Claire, Frank Burslem, and Jerry Lanfear. "Gene expression profiling of wound healing in keratinocytes using DNA microarrays." Nature Genetics 23, S3 (November 1999): 54. http://dx.doi.org/10.1038/14336.

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Peano, Clelia, Silvio Bicciato, Giorgio Corti, Francesco Ferrari, Ermanno Rizzi, Raoul JP Bonnal, Roberta Bordoni, et al. "Complete gene expression profiling of Saccharopolyspora erythraea using GeneChip DNA microarrays." Microbial Cell Factories 6, no. 1 (2007): 37. http://dx.doi.org/10.1186/1475-2859-6-37.

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Oshlack, A., A. E. Chabot, G. K. Smyth, and Y. Gilad. "Using DNA microarrays to study gene expression in closely related species." Bioinformatics 23, no. 10 (March 23, 2007): 1235–42. http://dx.doi.org/10.1093/bioinformatics/btm111.

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Wu, Thomas D. "Analysing gene expression data from DNA microarrays to identify candidate genes." Journal of Pathology 195, no. 1 (2001): 53–65. http://dx.doi.org/10.1002/1096-9896(200109)195:1<53::aid-path891>3.0.co;2-h.

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Antonescu, Cristina R., Kai Wu, Guoliang Leon Xing, Manqiu Cao, Yaron Turpaz, Margaret A. Leversha, Earl Hubbell, Robert G. Maki, C. Garrett Miyada, and Raji Pillai. "DNA Copy Number Analysis in Gastrointestinal Stromal Tumors Using Gene Expression Microarrays." Cancer Informatics 6 (January 2008): CIN.S387. http://dx.doi.org/10.4137/cin.s387.

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We report a method, Expression-Microarray Copy Number Analysis (ECNA) for the detection of copy number changes using Affymetrix Human Genome U133 Plus 2.0 arrays, starting with as little as 5 ng input genomic DNA. An analytical approach was developed using DNA isolated from cell lines containing various X-chromosome numbers, and validated with DNA from cell lines with defined deletions and amplifications in other chromosomal locations. We applied this method to examine the copy number changes in DNA from 5 frozen gastrointestinal stromal tumors (GIST). We detected known copy number aberrations

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Ebert, Benjamin L., and Todd R. Golub. "Genomic approaches to hematologic malignancies." Blood 104, no. 4 (August 15, 2004): 923–32. http://dx.doi.org/10.1182/blood-2004-01-0274.

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AbstractIn the past several years, experiments using DNA microarrays have contributed to an increasingly refined molecular taxonomy of hematologic malignancies. In addition to the characterization of molecular profiles for known diagnostic classifications, studies have defined patterns of gene expression corresponding to specific molecular abnormalities, oncologic phenotypes, and clinical outcomes. Furthermore, novel subclasses with distinct molecular profiles and clinical behaviors have been identified. In some cases, specific cellular pathways have been highlighted that can be therapeuticall

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Skotheim, Rolf I., Anne Kallioniemi, Bodil Bjerkhagen, Fredrik Mertens, Helge R. Brekke, Outi Monni, Spyro Mousses та ін. "Topoisomerase-IIα Is Upregulated in Malignant Peripheral Nerve Sheath Tumors and Associated With Clinical Outcome". Journal of Clinical Oncology 21, № 24 (15 грудня 2003): 4586–91. http://dx.doi.org/10.1200/jco.2003.07.067.

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Purpose: To identify target genes of clinical significance for patients with malignant peripheral-nerve sheath tumor (MPNST), an aggressive cancer for which no consensus therapy exists. Materials and Methods: Biopsies and clinical data from 51 patients with MPNST were included in this study. Based on our previous research implicating chromosome arm 17q amplification in MPNST, we performed gene expression analyses of 14 MPNSTs using chromosome 17–specific cDNA microarrays. Copy numbers of selected gene probes and centromere probes were then determined by interphase fluorescence in situ hybridiz

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Verducci, Joseph S., Vincent F. Melfi, Shili Lin, Zailong Wang, Sashwati Roy, and Chandan K. Sen. "Microarray analysis of gene expression: considerations in data mining and statistical treatment." Physiological Genomics 25, no. 3 (May 16, 2006): 355–63. http://dx.doi.org/10.1152/physiolgenomics.00314.2004.

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DNA microarray represents a powerful tool in biomedical discoveries. Harnessing the potential of this technology depends on the development and appropriate use of data mining and statistical tools. Significant current advances have made microarray data mining more versatile. Researchers are no longer limited to default choices that generate suboptimal results. Conflicting results in repeated experiments can be resolved through attention to the statistical details. In the current dynamic environment, there are many choices and potential pitfalls for researchers who intend to incorporate microar

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Beliaev, Alex S., Dorothea K. Thompson, Matthew W. Fields, Liyou Wu, Douglas P. Lies, Kenneth H. Nealson, and Jizhong Zhou. "Microarray Transcription Profiling of a Shewanella oneidensis etrA Mutant." Journal of Bacteriology 184, no. 16 (August 15, 2002): 4612–16. http://dx.doi.org/10.1128/jb.184.16.4612-4616.2002.

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ABSTRACT DNA microarrays were used to examine the effect of an insertional mutation in the Shewanella oneidensis etrA (electron transport regulator) locus on gene expression under anaerobic conditions. The mRNA levels of 69 genes with documented functions in energy and carbon metabolism, regulation, transport, and other cellular processes displayed significant alterations in transcript abundance in an etrA-mutant genetic background. This is the first microarray study indicating a possible involvement of EtrA in the regulation of gene expression in S. oneidensis MR-1.

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Aittokallio, Tero, Markus Kurki, Olli Nevalainen, Tuomas Nikula, Anne West, and Riitta Lahesmaa. "Computational Strategies for Analyzing Data in Gene Expression Microarray Experiments." Journal of Bioinformatics and Computational Biology 01, no. 03 (October 2003): 541–86. http://dx.doi.org/10.1142/s0219720003000319.

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Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinf

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Prasad, Alisha, Syed Mohammad Abid Hasan, Steven Grouchy, and Manas Ranjan Gartia. "DNA microarray analysis using a smartphone to detect the BRCA-1 gene." Analyst 144, no. 1 (2019): 197–205. http://dx.doi.org/10.1039/c8an01020j.

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McGrath, Ken C., Rhiannon Mondav, Regina Sintrajaya, Bill Slattery, Susanne Schmidt, and Peer M. Schenk. "Development of an Environmental Functional Gene Microarray for Soil Microbial Communities." Applied and Environmental Microbiology 76, no. 21 (September 17, 2010): 7161–70. http://dx.doi.org/10.1128/aem.03108-09.

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ABSTRACT Functional attributes of microbial communities are difficult to study, and most current techniques rely on DNA- and rRNA-based profiling of taxa and genes, including microarrays containing sequences of known microorganisms. To quantify gene expression in environmental samples in a culture-independent manner, we constructed an environmental functional gene microarray (E-FGA) consisting of 13,056 mRNA-enriched anonymous microbial clones from diverse microbial communities to profile microbial gene transcripts. A new normalization method using internal spot standards was devised to overco

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Ma, Chi, and Louis M. Staudt. "Molecular definition of the germinal centre stage of B–cell differentiation." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 356, no. 1405 (January 29, 2001): 83–89. http://dx.doi.org/10.1098/rstb.2000.0752.

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Genomic–scale gene expression analysis provides views of biological processes as a whole that are difficult to obtain using traditional single–gene experimental approaches. In the case of differentiating systems, gene expression profiling can define a stage of differentiation by the characteristic expression of hundreds of genes. Using specialized DNA microarrays termed ‘Lymphochips’, gene expression during mature B–cell differentiation has been defined. Germinal centre B cells represent a stage of differentiation that can be defined by a gene expression signature that is not shared by other h

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